A fast training method for AdaBoost classifier

To resolve the problem that the computation of training AdaBoost classifier increases sharply with the increase of candidate features and training samples, a fast training method for AdaBoost classifier is proposed. An AdaBoost classifier is composed of multiple decision stakes. Because of the randomness of the features values distribution of positive and negative samples, the existing methods exhaustively search the feature values in training samples to obtain the best decision stakes. First, based on the approximation convexity of the excellent feature’s threshold−error (T−E) curve, the binary search method is proposed to determine the best decision stakes. Compared with the exhaustive search method, the time complexity of comparison operation is reduced from O(N) to O\left( \log N \right). Secondly, with the revised function of error, the time complexity of the estimation classification error is reduced from O(N) to O(1). Finally, combining the above two, the fast training method of AdaBoost classifier is given. Experiments on tow public pedestrian detection image dataset, Inria pedestrian dataset and Caltech pedestrian detection benchmark show that the classifier trained by the fast training method is peer to that of the ordinary method on performance.